Instructions to use d-wang26/mk1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use d-wang26/mk1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("d-wang26/mk1", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Download diffusers/docs/source/en/api/loaders/textual_inversion.md from d-wang26/mk1: direct link, hf CLI and curl.
- Browser
- Download file 1.34 kB
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https://huggingface.co/d-wang26/mk1/resolve/main/diffusers/docs/source/en/api/loaders/textual_inversion.md
- Command line
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hf download hf://d-wang26/mk1/diffusers/docs/source/en/api/loaders/textual_inversion.md
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curl -L -o textual_inversion.md https://huggingface.co/d-wang26/mk1/resolve/main/diffusers/docs/source/en/api/loaders/textual_inversion.md
1.34 kB
Textual Inversion
Textual Inversion is a training method for personalizing models by learning new text embeddings from a few example images. The file produced from training is extremely small (a few KBs) and the new embeddings can be loaded into the text encoder.
[TextualInversionLoaderMixin] provides a function for loading Textual Inversion embeddings from Diffusers and Automatic1111 into the text encoder and loading a special token to activate the embeddings.
To learn more about how to load Textual Inversion embeddings, see the Textual Inversion loading guide.
TextualInversionLoaderMixin
[[autodoc]] loaders.textual_inversion.TextualInversionLoaderMixin